Abstract
Functional electrical stimulation (FES) and rehabilitation machines have the potential to recover lost function and mobility in people with neurological disorders. However, the human-robot system is uncertain, nonlinear, and time-varying posing technical challenges to customizing the interaction across participants. In this seminar, the design of closed-loop switching adaptive controllers to achieve cadence tracking using powered FES machines will be discussed. The proposed design copes with the parametric uncertainty of the cycle-rider dynamics and the unknown switching muscle control effectiveness by computing adaptive estimates of the uncertain parameters. For the last part of the seminar, the stability analysis using Lyapunov methods for switching systems will be presented to provide theoretical convergence guarantees followed by the preliminary experimental results obtained on the different testbeds.
Short CV
Jonathan Casas received the B.Sc. and M.Sc. degrees in electrical engineering from the Colombian School of Engineering Julio Garavito in Bogota, Colombia, in 2016 and 2019, respectively. He is currently with the Bionics, Systems, and Control Lab at Syracuse University, NY, USA pursuing a Ph.D. degree under the supervision of Dr. Victor Duenas. His research is focused on the design and evaluation of data-driven adaptive controllers for nonlinear systems. Specifically, the application of a concurrent learning technique for human-in-the-loop systems such as exoskeletons and rehabilitation machines.